A noise-based defense for stealthy backdoor attacks in large vision-language models
Control noise injection is investigated as a lightweight input-side defense against BadVision-style backdoors and suggests that stealthy encoder-level triggers depend on fragilestatistical patterns and can be weakened through controlled noise injection without requiring training of the full multimodal model.
James Patrick Donohue
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